Regional and pan-arctic climate and ecosystem models rely on land cover maps derived from remote sensing to estimate and predict associated ecosystem processes within the carbon cycle and the water and energy balance. Systematically operating satellite systems provide a regular coverage of arctic land cover but do not have sufficient resolution to resolve small-scale surface heterogeneity. To what degree this aggregation of land cover introduces error and uncertainty into the model outcome remains a crucial research question.
A multi-scale and multi-sensor strategy is used to map the fine scale spatial heterogeneity of northern permafrost landscapes regarding land cover, surface wetness and surface temperature.  High-resolution aerial images (0.3m per pixel) allow the validation of coarser scale remote sensing data like Landsat or MODIS. In combination with ground measurements of energy, water and carbon fluxes, the effect of aggregation on model outcome can be assessed.
Radar imagery like TerraSAR-X (6 m per pixel), ALOS Palsar (30 m per pixel) and ENVISAT-ASAR (75 m per pixel) are currently being explored for the use in seasonal and inter-annual land cover change detection, e.g. the mapping of water bodies, snow melt, soil moisture and refreeze.